Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Region Normalization for Image Inpainting
Authors: Tao Yu, Zongyu Guo, Xin Jin, Shilin Wu, Zhibo Chen, Weiping Li, Zhizheng Zhang, Sen Liu12733-12740
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our method outperforms current state-of-the-art methods quantitatively and qualitatively. |
| Researcher Affiliation | Academia | CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System, University of Science and Technology of China EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or explicitly labeled algorithm blocks. |
| Open Source Code | Yes | 1The codes are available at https://github.com/geekyutao/RN |
| Open Datasets | Yes | We evaluate our methods on Places2 (Zhou et al. 2017) and Celeb A (Liu et al. 2015) datasets. |
| Dataset Splits | No | The paper mentions testing on 'total validation data (36500 images) of Places2' but does not provide explicit train/validation/test dataset splits (e.g., percentages or counts) for the datasets used. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set threshold t = 0.8 in this work. We apply RN-B in the early layers (encoder) of our generator and RN-L in the intermediate and later layers (the residual blocks and decoder). |